Factors Associated With Fear of Coronavirus Disease 2019 Stigmatization in the Elderly

Objective@#The purpose of this study was to investigate the factors associated with the fear of coronavirus disease of 2019 (COVID-19) stigmatization in the elderly in Chungcheongnam-do, Korea. @*Methods@#This study used data of a survey on depression, loneliness, social support, and fear of COVID-19 of the elderly in a province of Korea in July to November, 2020. The sample consisted of 1,485 (female 1,117); mean age 78.3 (6.5) years old. We administered Korean version of the Geriatric Depression Scale-Short Form, 6-item De Jong Gierveld Loneliness Scale, Medical Outcome Study Social Support Survey and experience about COVID-19 infection or fear of stigmatization. Binary logistic regression analyses were performed for evaluating the association between fear of COVID-19 stigmatization and the factors; gender, education level, living with spouse, socioeconomic status, depression, loneliness, and social support. @*Results@#Socioeconomic status and loneliness are associated with the fear of COVID-19 disease stigmatization, and gender and socioeconomic status are associated with the fear of COVID-19 territorial stigmatization. @*Conclusion@#Gender, socioeconomic status and loneliness are the factors for COVID-19 stigmatization in the elderly in Chungcheongnam-do, Korea.

Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. RESULTS: The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). CONCLUSION: Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.

Functional Communication Profiles in Children with Cerebral Palsy in Relation to Gross Motor Function and Manual and Intellectual Ability

PURPOSE: The aim of the present study was to investigate communication function using classification systems and its association with other functional profiles, including gross motor function, manual ability, intellectual functioning, and brain magnetic resonance imaging (MRI) characteristics in children with cerebral palsy (CP). MATERIALS AND METHODS: This study recruited 117 individuals with CP aged from 4 to 16 years. The Communication Function Classification System (CFCS), Viking Speech Scale (VSS), Speech Language Profile Groups (SLPG), Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and intellectual functioning were assessed in the children along with brain MRI categorization. RESULTS: Very strong relationships were noted among the VSS, CFCS, and SLPG, although these three communication systems provide complementary information, especially for children with mid-range communication impairment. These three communication classification systems were strongly related with the MACS, but moderately related with the GMFCS. Multiple logistic regression analysis indicated that manual ability and intellectual functioning were significantly related with VSS and CFCS function, whereas only intellectual functioning was significantly related with SLPG functioning in children with CP. Communication function in children with a periventricular white matter lesion (PVWL) varied widely. In the cases with a PVWL, poor functioning was more common on the SLPG, compared to the VSS and CFCS. CONCLUSION: Very strong relationships were noted among three communication classification systems that are closely related with intellectual ability. Compared to gross motor function, manual ability seemed more closely related with communication function in these children.

Four-Dimensional Real-Time Cine Images of Wrist Joint Kinematics Using Dual Source CT with Minimal Time Increment Scanning

PURPOSE: To validate the feasibility of real time kinematography with four-dimensional (4D) dynamic functional wrist joint imaging using dual source CT. MATERIALS AND METHODS: Two healthy volunteers performed radioulnar deviation and pronation-supination wrist motions for 10 s and 4 s per cycle in a dual source CT scanner. Scan and reconstruction protocols were set to optimize temporal resolution. Cine images of the reconstructed carpal bone of the moving wrist were recorded. The quality of the images and radiation dosage were evaluated. RESULTS: The 4D cine images obtained during 4 s and 10 s of radioulnar motion showed a smooth stream of movement with good quality and little noise or artifact. Images from the pronation-supination motion showed noise with a masked surface contour. The temporal resolution was optimized at 0.28 s. CONCLUSION: Using dual source CT, 4D cine images of in vivo kinematics of wrist joint movement were obtained and found to have a shorter scan time, improved temporal resolution and lower radiation dosages compared with those previously reported.